It is common in the medical field to perform visual examination to diagnose disease. For example, visual examination of the cervix can discern areas where there is a suspicion of pathology. However, direct visual observation alone is often inadequate for identification of abnormalities in a tissue.
In some instances, when tissues of the cervix are examined in vivo, chemical agents such as acetic acid are applied to enhance the differences in appearance between normal and pathological areas. Aceto-whitening techniques may aid a colposcopist in the determination of areas where there is a suspicion of pathology.
However, colposcopic techniques generally require analysis by a highly trained physician. Colposcopic images may contain complex and confusing patterns. In colposcopic techniques such as aceto-whitening, analysis of a still image does not capture the patterns of change in the appearance of tissue following application of a chemical agent. These patterns of change may be complex and difficult to analyze. Current automated image analysis methods do not allow the capture of the dynamic information available in various colposcopic techniques.
Traditional image analysis methods include segmentation of individual images. Segmentation is a morphological technique that splits an image into different regions according to one or more pre-defined criteria. For example, an image may be divided into regions of similar intensity. It may therefore be possible to determine which sections of a single image have an intensity within a given range. If a given range of intensity indicates suspicion of pathology, the segmentation may be used as part of a diagnostic technique to determine which regions of an image may indicate diseased tissue.
However, standard segmentation techniques do not take into account dynamic information, such as a change of intensity over time. This kind of dynamic information is important to consider in various diagnostic techniques such as aceto-whitening colposcopy. A critical factor in discriminating between healthy and diseased tissue may be the manner in which the tissue behaves throughout a diagnostic test, not just at a given time. For example, the rate at which a tissue whitens upon application of a chemical agent may be indicative of disease. Traditional segmentation techniques do not take into account time-dependent behavior, such as rate of whitening.